ENTERPRISE RAG & MCP SYSTEMS

Generative AI
Application Development

Transition from proof-of-concept and unstructured AI prototypes to secure and reliable enterprise systems grounded in proprietary data. We deliver enterprise-grade RAG pipelines and MCP-enabled inference-time context augmentation, with security at the core.

Methodology

Approach Comparison

Choosing the right Gen AI technique depends on your data, latency requirements, and cost constraints. We help you select and combine approaches for optimal results.

RAG Architecture

Recommended

Retrieve relevant context from your knowledge base at query time. Best for dynamic, large-scale enterprise data.

Latency Medium overhead

Data Freshness Close to real-time

Cost per Query $0.05-$0.1

Best For Dynamic enterprise data, compliance-sensitive queries

Fine-Tuning

Specialized

Train the model on domain-specific data for specialized behavior. Best for consistent, pattern-based tasks.

Latency Low overhead

Data Freshness Training snapshot

Training Cost $50-$500+

Best For Domain-specific tone, performance sensitive workloads

Prompt Engineering

Lightweight

Craft precise instructions and few-shot examples. Best for rapid prototyping and simple extraction tasks.

Latency Low/Medium overhead

Data Freshness Context window only

Cost per Query $0.02-$0.05

Best For Prototyping, simple tasks, low-complexity use cases

Architecture

RAG Architecture Pipeline

A six-stage flow from raw data ingestion to verified, grounded responses — with LLM tool calling via MCP for dynamic data retrieval at inference time.

Data Ingestion

Structured and unstructured enterprise data enters the pipeline — documents, databases, APIs, and real-time streams.

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Vector Embedding

Content is chunked, tokenized, and transformed into high-dimensional vector representations via fit for purpose embedding models.

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Semantic Retrieval

User queries are vectorized and matched against the knowledge base using cosine similarity for precise, context-aware retrieval.

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LLM Augmentation

Retrieved context is injected into the prompt alongside the user query, grounding the LLM's response in verified enterprise data.

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LLM Tool Calling

During inference, if needed, the LLM dynamically requests additional data, and it is retrieved via the MCP (Model Context Protocol) standard — extending context beyond static retrieval.

MCP Protocol

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Verified Response

The final output is grounded, cited, and validated — reducing hallucination risk and delivering trustworthy answers to end users.

Differentiator

MCP-Enabled Inference

We build custom MCP Servers that give organizations secure, dynamic access to their own data — a more efficient alternative to classic RAG pipelines.

Our MCP Approach

Security-First

✓ Network isolation & private endpoints for all MCP server communication
✓ AuthN/AuthZ policies enforced
✓ Comprehensive audit logging for every tool call and data access
✓ Integrated with Azure cloud security & compliance embedded at the core
✓ Custom MCP Servers tailored to organizational data topology

Standard MCP Implementations

Risk Exposure

✗ Data exfiltration risk — tool calls may leak sensitive data to external endpoints
✗ No built-in network isolation or enterprise boundary enforcement
✗ Minimal audit trail for compliance-sensitive environments
✗ Generic implementations ignore cloud security posture
✗ One-size-fits-all servers not tailored to enterprise data governance

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Generative AI Application Development